Nanoscale Connectomics Annotation Standards Framework.

The promise of large-scale, high-resolution datasets from Electron Microscopy (EM) and X-ray Microtomography (XRM) lies in their ability to reveal neural structures and synaptic connectivity, which is critical for understanding the brain. Effectively managing these complex and rapidly increasing datasets will enable new scientific insights, facilitate querying, and support secondary use across the neuroscience community. However, without effective neurodata standards that permit use of these data across multiple systems and workflows, these valuable and costly datasets risk being underutilized especially as they surpass petascale levels.

NEURD offers automated proofreading and feature extraction for connectomics.

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution. Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML). Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post-hoc proofreading is still required to generate large connectomes free of merge and split errors.

Tales of diversity: Genomic and morphological characteristics of forty-six Arthrobacter phages.

The vast bacteriophage population harbors an immense reservoir of genetic information. Almost 2000 phage genomes have been sequenced from phages infecting hosts in the phylum Actinobacteria, and analysis of these genomes reveals substantial diversity, pervasive mosaicism, and novel mechanisms for phage replication and lysogeny. Here, we describe the isolation and genomic characterization of 46 phages from environmental samples at various geographic locations in the U.